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Gaussian Processes

Gaussian Processes is a powerful framework for several machine learning tasks such as regression, classification and inference. Given a finite set of input output training data that is generated out of a fixed (but possibly unknown) function, the framework models the unknown function as a stochastic process such that the training outputs are a finite number of jointly Gaussian random variables, whose properties can then be used to infer the statistics (the mean and variance) of the function at test values of input.

Source: Sequential Randomized Matrix Factorization for Gaussian Processes: Efficient Predictions and Hyper-parameter Optimization

Papers

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Showing 1601–1610 of 1963 papers

TitleStatusHype
Efficiently Learning Nonstationary Gaussian Processes for Real World Impact—0
Convergence and Concentration of Empirical Measures under Wasserstein Distance in Unbounded Functional Spaces—0
Adaptive Sensing for Learning Nonstationary Environment Models—0
Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data—0
Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio—0
Provably Robust Learning-Based Approach for High-Accuracy Tracking Control of Lagrangian Systems—0
Quantum algorithms for training Gaussian Processes—0
Scalable Generalized Dynamic Topic ModelsCode0
Meta Reinforcement Learning with Latent Variable Gaussian Processes—0
Learning non-Gaussian Time Series using the Box-Cox Gaussian Process—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ICKy, periodicRoot mean square error (RMSE)0.03—Unverified